Omics data

With a focus on high-throughput sequencing data

Jelmer Poelstra

CFAES Bioinformatics Core, OSU

2025-08-26

An overview of omics data

The main omics data types

  • Genomics (including metagenomics, epigenomics, etc.)
  • Transcriptomics (including translatomics)
  • Proteomics
  • Metabolomics

Both genomics and transcriptomics data is produced by high-throughput sequencing technologies.

That will be the focus of this lecture and will be used in examples throughout the course.

Intro to sequencing technologies

What does sequencing refer to?

The shorthand sequencing, like in “high-throughput sequencing”, generally refers to determining the nucleotide sequence of fragments of DNA.


What about RNA or proteins?

  • RNA is usually reverse transcribed to DNA (cDNA) prior to sequencing, as in nearly all “RNA-seq”.

    Direct RNA sequencing is possible with one of the sequencing technologies we’ll discuss, but this is under development and not yet widely used.

  • Protein sequencing requires different technology altogether, such as mass spectrometry, and is not further discussed in this lecture.

Sequencing technologies: overview

Sanger sequencing (since 1977)
Sequences a single, typically PCR-amplified, short-ish (≤900 bp) DNA fragment at a time


High-throughput sequencing (HTS, since 2005)
Sequences 105-109s, usually randomly selected, DNA fragments (“reads”) at a time

Sequencing technology development timeline

Modified after Pereira et al. 2020

Sequencing technology development timeline

Modified after Pereira et al. 2020

Sequencing cost through time

https://www.genome.gov/about-genomics/fact-sheets/Sequencing-Human-Genome-cost

High-throughput sequencing (HTS)

HTS applications

  • Whole-genome assembly

  • Variant analysis (for population genetics/genomics, molecular evolution, GWAS, etc.):

    • Whole-genome “resequencing”

    • Reduced-representation libraries (e.g. RADseq, GBS)

  • RNA-seq (transcriptome analysis)
  • Other functional sequencing methods like methylation sequencing, ChIP-seq, etc.

  • Microbial community characterization

    • Metabarcoding

    • Shotgun metagenomics

The main HTS types

Short-read HTS

  • AKA Next-Generation Sequencing (NGS)
  • Produces up to billions of 50-300 bp reads
  • Market dominated by Illumina
  • Since 2005 — technology fairly stable

Long-read HTS

  • Reads much longer than in NGS but fewer, less accurate, and more costly per base
  • Two main companies: Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio)
  • Since 2011 — remains under rapid development


Short videos explaining the technology (90 s - 5 m each)

Read lengths

  • Short-read (Illumina) HTS: 50-300 bp reads

  • Long-read HTS: longer & more variable read lengths (PacBio: 10-50 kbp, ONT: 10-100+ kbp)

When are longer reads useful?

  • Genome assembly

  • Haplotype and large structural variant calling

  • Transcript isoform identification

  • Taxonomic identification of single reads (microbial metabarcoding)

When does read length not matter (as much)?

  • SNP variant analysis

  • Read-as-a-tag: the goal is just to know a read’s origin in a reference genome, like in counting applications such as RNA-seq

Error rates

Currently, no sequencing technology is error-free.

  • Illumina error rates are mostly below 0.1%
  • TBA

Error rates are changing

Error rates in one recent type of PacBio sequencing where individual fragments are sequenced multiple times (“HiFi”) are now lower than in Illumina.

Error rates of ONT sequencing are also continuously decreasing.


Quality scores in sequence data

When you get sequences from a high-throughput sequencer, base calls have typically already been made. Every base is also accompanied by a quality score (inversely related to the estimated error probability). We’ll talk about those in some more detail in a bit.

Overcoming sequencing errors

Sequencing every bases multiple times, i.e. having a >1x so-called “depth of coverage” allows to infer the correct sequence:

  • Overcoming sequencing errors is made more challenging by natural genetic variation among and within individuals.

  • Typical depths of coverage: at least 50-100x for genome assembly; 10-30x for resequencing.

Illumina libraries

Libraries and library prep

In a sequencing context, a “library” is a collection of nucleic acid fragments ready for sequencing.

In Illumina and other HTS libraries, these fragments number in the millions or billions and are often randomly generated from input such as genomic DNA:

This procedure is called library prep, and is typically done for you by a sequencing facility or company.

Libraries and library prep (cont.)

After library prep (here, for Illumina sequencing), each DNA fragment is flanked by several types of short sequences that together make up the “adapters”:



Paired-end vs. single-end sequencing

In Illumina sequencing, DNA fragments can be sequenced from both ends as shown below — this is called “paired-end” (PE) sequencing:

When sequencing is instead single-end (SE), no reverse read is produced:

Insert size

The total size of the biological DNA fragment (without adapters) is often called the insert size:

Insert size variation

Insert size varies — because the library prep protocol can aim for various sizes, and because of variation due to limited precision in size selection. In some cases, the insert size can be:

  • Shorter than the combined read length, leading to overlapping reads (this can be useful):

  • Shorter than the single read length, leading to “adapter read-through” (i.e., the ends of the resulting reads will consist of adapter sequence, which should be removed):

Multiplexing!

Using the indices/barcodes in adapters, up to 96 samples can be multiplexed into a single library.

Reference genomes

Genomes

Most HTS applications either require a “reference genome” or involve its production.

What exactly does “reference genome” refer to? We’ll discuss three components to this phrase:

  • Assembly
    It includes a representation of most of the genome DNA sequence: the genome assembly
  • Annotation
    It (preferably) includes an “annotation” that provides the locations of genes and other genomic features, as well as functional information on these features

  • Taxonomic identity
    Typically considered at the species level, so then it should involve the focal species. But:

    • If needed, it is often possible to work with reference genomes of closely related species

    • Conversely, multiple reference genomes may exist, e.g. for different subspecies

Genome size variation

https://en.wikipedia.org/wiki/Genome_size

https://en.wikipedia.org/wiki/Genome_size

Growth of genome databases

Konkel & Slot 2023

Genome assemblies

  • With increasing usage & quality of long-read HTS, we are generating better assemblies

  • For chromosome-level assemblies, i.e. with one contiguous sequence for each chromosome, additional technologies than sequencing are often needed (e.g. Hi-C, optical mapping)

  • Many assemblies are not “chromosome-level”, but consist of –often 1000s of– contigs and scaffolds.

  • Even chromosome-level assemblies are not 100% complete


Question: Contigs vs. scaffolds?

Contigs are contiguous, known stretches of DNA created by the assembly process, basically by overlapping reads.

Often, the order and orientation of two or more contigs is known, but there is a gap of unknown size between them. Such contigs are connected into scaffolds with a stretch of Ns in between.

Overview

All common genetic/genomic data files are plain-text, meaning that they can be opened by any text editor. However, they are often compressed to save space. The main types are:

  • FASTA
    Simple sequence files, where each entry contains a header and a DNA/AA sequence.
    Versatile, anything from a genome assemblies, proteomes, and single sequence fragments to alignments can be in this format.

  • FASTQ
    The standard format for HTS reads — contains a quality score for each nucleotide.

  • SAM/BAM
    An alignment format for HTS reads.

  • GTF/GFF
    Tables (tab-delimited) with information such as genomic coordinates on “genomic features” such as genes and exons. The files contain reference genome annotations.

FASTA files

FASTA files contain one or more (sometimes called multi-FASTA) DNA or amino acid sequences, with no limits on the number of sequences or the sequence lengths.

As mentioned, they are versatile, and are the standard format for:

  • Genome assembly sequences

  • Transcriptomes and proteomes

  • Sequence downloads from NCBI such as a single gene/protein or other GenBank entry

  • Sequence alignments (but not from HTS reads)

FASTA files (cont.)

The following example FASTA file contains two entries:

>unique_sequence_ID Optional description
ATTCATTAAAGCAGTTTATTGGCTTAATGTACATCAGTGAAATCATAAATGCTAAAAA
>unique_sequence_ID2
ATTCATTAAAGCAGTTTATTGGCTTAATGTACATCAGTGAAATCATAAATGCTAAATG

Each entry contains a header and the sequence itself, and:

  • Header lines start with a > and are otherwise “free form” but usually provide an identifier (and sometimes metadata) for the sequence
  • The sequence can be spread across multiple lines with a fixed width

FASTA file name extensions are variable:

  • Generic extensions are .fasta and .fa

  • Other extensions explicitly indicate whether sequences are nucleotide (.fna) or amino acids (.faa)

FASTQ

FASTQ is the standard format for HTS reads.
Each read forms one FASTQ entry and is represented by four lines, which contain, respectively:

  1. A header that starts with @ and e.g. uniquely identifies the read
  2. The sequence itself
  3. A + (plus sign)
  4. One-character quality scores for each base (hence FASTQ as in “Q” for “quality”)

FASTQ quality scores

The quality scores we saw in the read on the previous slide represent an estimate of the error probability of the base call.

Specifically, they correspond to a numeric “Phred” quality score (Q), which is a function of the estimated probability that a base call is erroneous (P):

Q = -10 * log10(P)

For some specific probabilities and their rough qualitative interpretation for Illumina data:

Phred quality score Error probability Rough interpretation
10 1 in 10 terrible
20 1 in 100 bad
30 1 in 1,000 good
40 1 in 10,000 excellent

FASTQ quality scores (cont.)

This numeric quality score is represented in FASTQ files not by the number itself, but by a corresponding “ASCII character”.

This allows for a single-character representation of each possible score — as a consequence, each quality score character can conveniently correspond to (& line up with) a base character in the read.

Phred quality score Error probability ASCII character
10 1 in 10 +
20 1 in 100 5
30 1 in 1,000 ?
40 1 in 10,000 I

A rule of thumb

In practice, you almost never have to manually check the quality scores of bases in FASTQ files, but if you do, a rule of thumb is that letter characters are good (Phred of 32 and up).

FASTQ (cont.)

FASTQ files have no size limit, so you may receive a single file per sample, although:

  • With paired-end (PE) sequencing, forward and reverse reads are split into two files:
    forward reads contain R1 and reverse reads contain R2 in the file name.

  • If sequencing was done on multiple “lanes”, you get separate files for each lane.

FASTQ files have the extension .fastq or .fq (but are commonly compressed, leading to fastq.gz etc.). All in all, having paired-end FASTQ files for 2 samples could look like this:

# A listing of (unusually simple) file names:
sample1_R1.fastq.gz
sample1_R2.fastq.gz
sample2_R1.fastq.gz
sample2_R1.fastq.gz

GTF/GFF

The GTF and GFF formats are tab-delimited tabular files that contain genome annotations, with:

  • One row for each annotated “genomic feature” (gene, exon, etc.)

  • One column for each piece of information about a feature, like its genomic coordinates

See the sample below, with an added header line (not normally present) with column names:

seqname     source  feature start   end     score  strand  frame    attributes
NC_000001   RefSeq  gene    11874   14409   .       +       .       gene_id "DDX11L1"; transcript_id ""; db_xref "GeneID:100287102"; db_xref "HGNC:HGNC:37102"; description "DEAD/H-box helicase 11 like 1 (pseudogene)"; gbkey "Gene"; gene "DDX11L1"; gene_biotype "transcribed_pseudogene"; pseudo "true"; 
NC_000001   RefSeq  exon    11874   12227   .       +       .       gene_id "DDX11L1"; transcript_id "NR_046018.2"; db_xref "GeneID:100287102"; gene "DDX11L1"; product "DEAD/H-box helicase 11 like 1 (pseudogene)"; pseudo "true";

Some details on the more important/interesting columns:

  • seqname — Name of the chromosome, scaffold, or contig
  • feature — Name of the feature type, e.g. “gene”, “exon”, “intron”, “CDS”
  • start & end— Start & end position of the feature
  • strand — Whether the feature is on the + (forward) or - (reverse) strand
  • attribute — A semicolon-separated list of tag-value pairs with additional information

SAM/BAM

Using specialized bioinformatics tools, you can align HTS reads (in FASTQ files) to a reference genome assembly (in a FASTA file).

The resulting alignments are stored in the SAM (uncompressed) / BAM (compressed) format.

SAM/BAM are tabular files with one line per alignment, each of which includes:

  • The position in the genome that the read aligned to

  • A mapping score based on the length of the alignment and the number of mismatches

  • The sequence of aligned the read itself

File conversions

  • FASTQ files can be converted to FASTA files (losing quality information) but not vice versa

  • SAM/BAM files can be converted to FASTQ files (losing alignment information) but not vice versa

  • Proteome FASTA files can be produced from the combination of a FASTA genome assembly and a GFF/GTF genome annotation

Questions?





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